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tie framework works roughly

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This commit is contained in:
Zhenwen Dai 2014-06-23 19:39:35 +01:00
parent fa6b5343cc
commit 567612b3a9
2 changed files with 33 additions and 20 deletions
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49
GPy/core/parameterization/parameter_core.py
View file

@ -423,12 +423,15 @@ class Indexable(Nameable, Observable):
if np.all(self._fixes_): self._fixes_ = None # ==UNFIXED
def _connect_fixes(self):
fixed_indices = self.constraints[__fixed__]
if fixed_indices.size > 0:
self._ensure_fixes()
self._fixes_[fixed_indices] = FIXED
else:
self._fixes_ = None
from ties_and_remappings import Tie
self._ensure_fixes()
# for c, ind in self.constraints.iteritems():
# if c == __fixed__ or isinstance(c,Tie):
# self._fixes_[ind] = FIXED
[np.put(self._fixes_, ind, FIXED) for c, ind in self.constraints.iteritems()
if c == __fixed__ or isinstance(c,Tie)]
if np.all(self._fixes_): self._fixes_ = None
if self.constraints[__fixed__]==0:
del self.constraints[__fixed__]
#===========================================================================
@ -501,29 +504,25 @@ class Indexable(Nameable, Observable):
old_const = self.constraints.properties()[:]
self.unconstrain()
#set these parameters to be 'fixed' as in, not optimized
self._highest_parent_._set_fixed(self, self._raveled_index())
#see if a tie exists with that name
if name in self._highest_parent_.ties:
t = self._highest_parent_.ties[name]
else:
#create a tie object
value = np.atleast_1d(self.param_array)[0]*1
import ties_and_remappings
t = ties_and_remappings.Tie(value=value, name=name)
from ties_and_remappings import Tie
t = Tie(value=value, name=name)
#add the new tie object to the global index
self._highest_parent_.ties[name] = t
self._highest_parent_.add_parameter(t)
#constrain the tie as we were constrained
if len(old_const)==1:
t.constrain(old_const[0])
self.constraints.add(t, self._raveled_index())
t.add_tied_parameter(self)
self._highest_parent_._connect_fixes()
def constrain(self, transform, warning=True, trigger_parent=True):
"""
@ -649,10 +648,12 @@ class OptimizationHandlable(Indexable):
def _get_params_transformed(self):
# transformed parameters (apply un-transformation rules)
p = self.param_array.copy()
[np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
from ties_and_remappings import Tie
[np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__ and not isinstance(c,Tie)]
if self.has_parent() and self.constraints[__fixed__].size != 0:
fixes = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED
[np.put(fixes,ind,FIXED) for c, ind in self.constraints.iteritems()
if c == __fixed__ or isinstance(c,Tie)]
return p[fixes]
elif self._has_fixes():
return p[self._fixes_]
@ -664,15 +665,21 @@ class OptimizationHandlable(Indexable):
This means, the optimizer sees p, whereas the model sees transformed(p),
such that, the parameters the model sees are in the right domain.
"""
from ties_and_remappings import Tie
if not(p is self.param_array):
if self.has_parent() and self.constraints[__fixed__].size != 0:
fixes = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED
# fixes[self.constraints[__fixed__]] = FIXED
for c, ind in self.constraints.iteritems():
if c == __fixed__ or isinstance(c,Tie):
fixes[ind] = FIXED
self.param_array.flat[fixes] = p
elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
else: self.param_array.flat = p
[np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
for c, ind in self.constraints.iteritems() if c != __fixed__]
[np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
for c, ind in self.constraints.iteritems() if c != __fixed__ and not isinstance(c,Tie)]
[np.put(self.param_array, ind, c.val)
for c, ind in self.constraints.iteritems() if isinstance(c,Tie)]
self._trigger_params_changed()
def _trigger_params_changed(self, trigger_parent=True):
@ -699,7 +706,9 @@ class OptimizationHandlable(Indexable):
"""
if self.has_parent():
return g
[np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
from ties_and_remappings import Tie
[np.put(g, self._raveled_index_for(c.val), g[i].sum()) for c, i in self.constraints.iteritems() if isinstance(c,Tie)]
[np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__ and not isinstance(c,Tie)]
if self._has_fixes(): return g[self._fixes_]
return g

4
GPy/core/parameterization/ties_and_remappings.py
View file

@ -54,6 +54,7 @@ class Tie(Remapping):
index = self._highest_parent_.constraints[self]
if len(index)==0:
return # nothing to tie together, this tie exists without any tied parameters
self.value.gradient[:] = self._highest_parent_.gradient[index].sum()
vals = self._highest_parent_.param_array[index]
uvals = np.unique(vals)
if len(uvals)==1:
@ -66,6 +67,9 @@ class Tie(Remapping):
else:
#more than one of the tied things changed. panic.
raise ValueError, "something is wrong with the tieing"
def parameters_changed(self):
super(Tie,self).parameters_changed()
self.value.gradient[:] = self._highest_parent_.gradient[self._highest_parent_.constraints[self]].sum()
def mapping(self):
return self.value